Showing papers by "Pietro Ferraro published in 2021"

Roadmap on digital holography [Invited].

Abstract: This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

Rolling angle recovery of flowing cells in holographic tomography exploiting the phase similarity

Abstract: Holographic tomography allows the 3D mapping of the refractive index of biological samples thanks to reconstruction methods based on the knowledge of illumination directions or rotation angles of the imaged sample. Recently, phase contrast tomographic flow cytometry by digital holography has been demonstrated to reconstruct the three-dimensional refractive index distribution of single cells while they are flowing along microfluidic channels. In this system, the illumination direction is fixed while the sample’s rotation is not deterministically known a priori but induced by hydrodynamic forces. We propose here a technique to retrieve the rolling angles, based on a new phase images similarity metric that is capable of identifying a cell’s orientations from its 3D positioning while it is flowing along the microfluidic channel. The method is experimentally tested and also validated through appropriate numerical simulations. We provide demonstration of concept by achieving reconstruction of breast cancer cells tomography.

Three-Dimensional Quantitative Intracellular Visualization of Graphene Oxide Nanoparticles by Tomographic Flow Cytometry.

Abstract: Interaction of nanoparticles (NPs) with cells is of fundamental importance in biology and biomedical sciences. NPs can be taken up by cells, thus interacting with their intracellular elements, modifying the life cycle pathways, and possibly inducing death. Therefore, there is a great interest in understanding and visualizing the process of cellular uptake itself or even secondary effects, for example, toxicity. Nowadays, no method is reported yet in which 3D imaging of NPs distribution can be achieved for suspended cells in flow-cytometry. Here we show that, by means of label-free tomographic flow-cytometry, it is possible to obtain full 3D quantitative spatial distribution of nanographene oxide (nGO) inside each single flowing cell. This can allow the setting of a class of biomarkers that characterize the 3D spatial intracellular deployment of nGO or other NPs clusters, thus opening the route for quantitative descriptions to discover new insights in the realm of NP-cell interactions.

Sensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning.

Abstract: We present sensing time-lapse morphogenesis of living bone cells under micro-fluidic shear stress (FSS) by digital holographic (DH) microscopy. To remove the effect of aberrations on quantitative measurements, we propose a numerical and automatic method to compensate for aberrations based on a convolutional neural network (CNN). For the first time, the aberration compensation issue is considered as a regression task where optimal coefficients for constructing the phase aberration map act as responses corresponding to the input aberrated phase image. We adopted tens of thousands of living cells' phase images reconstructed from digital holograms for training the CNN. The experiments demonstrate that, based on the trained network, phase aberrations can be totally removed in real-time without any hypothesis of object and aberration phase, knowledge of the setup's physical parameters, and the operation of selecting background regions; hence, the morphogenesis of the bone cells under FSS is accurately detected and quantitatively analyzed. The results show that the proposed method could provide a highly efficient and versatile way to investigate the effects of micro-FSS on living biological cells in microfluidic lab-on-chip platforms thanks to the combination of phase-contrast label-free microcopy with artificial intelligence.

Miscalibration-Tolerant Fourier Ptychography

Abstract: Fourier Ptychography probes the sample from different directions to achieve label-free quantitative phase imaging with a large space-bandwidth product. However, special attention has to be paid in the calibration of the optical setup to assure the accurate knowledge of the geometrical parameters involved in the image reconstruction. Any slight misalignment can provoke incorrect synthesis of the observables and, in turn, severe phase errors in the resulting high-resolution image. Here, we present a new processing pipeline that automatically removes such a priori unknown artifacts, thus making Fourier Ptychography miscalibration-tolerant. This result is achieved through a numerical Multi-Look approach that generates and combines multiple reconstructions of the same set of observables where phase artifacts are largely uncorrelated and, thus, automatically suppress each other. The proposed method is non-iterative, fully parallelizable, and completely blind, unlocking the use of Fourier Ptychography as an easy to handle tool or add-on to existing microscopes to be employed by unskilled users, thus paving the way to biomedical and clinical practices.

Dehydration of plant cells shoves nuclei rotation allowing for 3D phase-contrast tomography.

Abstract: Single-cell phase-contrast tomography promises to become decisive for studying 3D intracellular structures in biology. It involves probing cells with light at wide angles, which unfortunately requires complex systems. Here we show an intriguing concept based on an inherent natural process for plants biology, i.e., dehydration, allowing us to easily obtain 3D-tomography of onion-epidermal cells' nuclei. In fact, the loss of water reduces the turgor pressure and we recognize it induces significant rotation of cells' nuclei. Thanks to the holographic focusing flexibility and an ad-hoc angles' tracking algorithm, we combine different phase-contrast views of the nuclei to retrieve their 3D refractive index distribution. Nucleolus identification capability and a strategy for measuring morphology, dry mass, biovolume, and refractive index statistics are reported and discussed. This new concept could revolutionize the investigation in plant biology by enabling dynamic 3D quantitative and label-free analysis at sub-nuclear level using a conventional holographic setup.

Active Thermography for Non-invasive Inspection of an Artwork on Poplar Panel: Novel Approach Using Principal Component Thermography and Absolute Thermal Contrast

Abstract: The conservation of the works of art represent a topic of global interest. The development of effective tools based on advanced technology for analysing and monitoring their health-state is essential to assuring their preservation. In fact, detecting and preventing the formation of defective areas or assessing for an accurate pre-restoration analysis are the main objectives of non-destructive inspection. Active thermography is a well-known non-invasive imaging technique and reliable tool for providing a fast and low-cost analysis of a work of art. In this study we combine the potential of Principal Component Thermography and of Absolute Thermal Contrast to analyse thermal images acquired in-situ on a poplar panel painting representing an original artwork dating in the end of XVI century. We first optimized the thermal stimulation parameters in the laboratory using special phantom samples. These samples were specially made by reproducing in high fidelity the structural properties and materials of the artwork in order to perform effectively the preliminary tests. Then we moved the equipment in-situ by performing the non-destructive inspection directly on the real artwork. We have developed a specific experimental protocol that combines active thermography with two specific and appropriate image processing modalities that allowed us the effective detection of various types of defects in the painting layer. We report a complete analysis and deep discussion concerning the detection and characterization of the defects. Results show that our diagnostic protocol is a powerful tool in assessing the pre-restoration health-state and suitable for in situ analysis of wood artworks.

Neuroblastoma Cells Classification Through Learning Approaches by Direct Analysis of Digital Holograms

Abstract: The label-free single cell analysis by machine and Deep Learning, in combination with digital holography in transmission microscope configuration, is becoming a powerful framework exploited for phenotyping biological samples. Usually, quantitative phase images of cells are retrieved from the reconstructed complex diffraction patterns and used as inputs of a deep neural network. However, the phase retrieval process can be very time consuming and prone to errors. Here we address the classification of cells by using learning strategies with images coming directly from the raw recorded digital holograms, i.e. without any data processing or refocusing involved. Indeed, in the raw digital hologram the entire complex amplitude information of the sample is intrinsically embedded in the form of modulated fringes. We develop a training strategy, based on deep and feature based machine learning models, in order extract such information by skipping the classical reconstruction process for classifying different neuroblastoma cells. We provided an experimental validation by using the proposed strategy to classify two neuroblastoma cell lines.

Transmitting Light Through Biocompatible and Biodegradable Drug Delivery Micro Needles

Abstract: The use of micro-needle for advanced self-administration and cutaneous therapy still represent a desired solution that could open towards still unexplored medical market. Recently we have developed a fabrication method that avoids one of the major drawbacks of conventional processes by using a mold-less direct fabrication approach based on electro-drawing of microneedle from sessile drops of biodegradable polymer. The method is completely contact-free, simple and low-cost. On the other side, the intriguing future developments of biodegradable microneedles is the possibility to functionalize and use the microneedles for phototherapy, and/or light assisting for in-situ drug activation or other functionalization that could require light delivery. Here we show that it is possible to fabricate microneedles able to focus and transmit light. In particular, we report how an electro-drawn microneedle, realized by a single-step process, can be used as a sort of light guiding micro-photonic element at the aim to delivery light form its tip. We demonstrate the light guiding properties of these conical structures showing preliminary modelling results combined with the experimental optical characterization. We believe that the proposed approach could be further exploited and could inspire future fabrication of smart nanobiophotonic devices combing multiple functionalities for implantable medicine and drug-delivery applications.

Quantitative determination of rapid biomass formation on pyro-electrified polymer sheets

Abstract: The ability of a bacterial strain to form a biofilm is strictly related to its pathogenicity. Bacterial adherence and early biofilm formation are influenced by chemical, physical and biological factors that determine their pathogenic properties. We recently presented in literature the ability of pyro-electrified polymer sheets to promote rapid biofilm formation, based on what we called biofilm electrostatic test (BET) carriers. Here we performed a step forward by presenting a comprehensive characterization of the BET methodology through a quantitative evaluation of the biomass on the BET-carrier in the very early stages of incubation. Two bacterial suspensions of Escherichia coli were added to the surface of the BET-carrier, with one order of magnitude difference in initial optical density. The biofilms were stained at different incubation times, while the crystal violet assay and the live/dead reaction kit were used for evaluating the biomass and the viability, respectively. The BET-carrier systematically promoted a faster biofilm formation even in case of very diluted bacterial concentration. The results suggest that the BET-carrier could be used for evaluating rapidly the ability of bacteria to form biofilms and thus their inclination to pathogenicity, thanks to the challenging acceleration in biofilm formation.

Mechanical and morphological characterization of 3D-printed carbonPEEK composite for avionic shimming

Abstract: In aerospace, it is of great interest to identify safe and low-cost manufacturing procedures to guarantee reliable and functional products. In this perspective, 3D printing is a fast and low-cost innovative technique to produce fiber-reinforced polymer matrix composites. This new technology, among other, forms short fiber composite that can be used as a shim material to fill voids left by manufacturing defects. The purpose of this work is to characterize reinforced composites developed for these applications and obtained with the innovative 3D printing technique.

An investigation on deep learning approaches for diatoms classification

Abstract: Diatoms are one of the largest groups of microalgae present in marine, freshwater and transitional environments and their reactivity to environmental changes makes them suitable to be employed as biomarkers for monitoring tasks. Anyway, their presence in a large number of species makes it arduous to perform diatoms taxonomy during monitoring tasks considering that, to date, analysis is conducted by marine biologists on the basis of their own experience and, hence, in a subjective way. Hence, the need for automatic and objective methodologies for the identification and classification of diatoms samples rises. Research efforts in the field of Computer Vision led to a plethora of highly effective deep learning strategies surpassing human capabilities for image classification, as showed in the recent Imagenet challenge editions where they were initially introduced. Despite the very promising results of the proposed solutions, the difficulty arises to determine which technique is most suitable among them for real tasks and in particular for diatoms classification. This work proposes an end-to-end pipeline for automatic recognition of diatoms, acquired by means of holographic microscopy in water samples, employing deep learning techniques. In particular the most recently introduced Convolution Neural Networks (CNNs) architectures have been deeply investigated and compared in order to highlight the pros and cons of each of them. Moreover, in order to feed the CNNs training stages with a suitable amount of labeled data, a strategy to build a synthetic dataset, starting from a single image per class available from commercial glass slides specifically prepared for taxonomy purposes, is introduced. Besides, models ensembling strategies, in order to improve the single model scores, have been exploited. Finally, the proposed approach has been validated employing a dataset built up of holographic images of diatoms sampled in natural water bodies.

Investigation of the influence of plasma treatment of flax fiber fabrics on the mechanical properties of polypropylene-based composites

Abstract: Composites represent the evolution of the material science and technologies. They are obtained by combining two or more materials of different nature with the aim of exploiting any synergies between the characteristic performances of the raw materials. Their properties, in fact, are influenced by those of the starting components but also by the quality of the interface generated between the combined phases as well as by their mutual distribution. The interphase, even if of minimal extension with respect to the main phases constituting the composite, plays a significant role in the control of the damage mechanisms, determines the breaking strength and the stress / deformation behavior of composite materials. In this work we study the effect of low pressure plasma treatment on intrinsically hydrophilic flax fiber fabrics to improve their adhesion to a hydrophobic polypropylene matrix. The fibers are treated using nitrogen (N2) plasma with four different exposure times. The interfacial adhesion actually achieved was indirectly quantified by interlaminar shear strength measurements. After this, the damaged areas were measured with non-destructive techniques, i.e. Electron Speckle Pattern Interferometry and Lock-in thermography.

Digital Holography and 3D Imaging 2020: introduction to the feature issue.

Abstract: This feature issue of JOSA A and Applied Optics is dedicated to the fourteenth OSA Topical Meeting "Digital Holography and 3D Imaging" held 22-26 June 2020 in a virtual meeting. The conference, taking place every year, is a focal point for global technical interchange in the field of digital holography and 3D imaging, providing premier opportunities for people working in the field to present their new advances in research and development. Papers presented at the meeting highlight current research in digital holography and three-dimensional imaging, including interferometry, phase microscopy, phase retrieval, novel holographic processes, 3D and novel holographic displays, integral imaging, computer-generated holograms, compressive holography, 3D holographic display, AR display, full-field tomography, specific image and signal processing, and holography with various light sources, including coherent to incoherent and x-ray to terahertz waves. Techniques of digital holography and of 3D imaging have numerous applications, such as the state-of-the-art technological developments that are currently underway and have also stimulated further novel applications of digital holography and 3D imaging in biomedicine, deep learning, and scientific and industrial metrologies.

Compact holographic microscope for imaging flowing microplastics

Abstract: The presence of dispersed plastic particulates in the environment is an ecological problem of increasing relevance, posing in addition potential health risks as they enter the food chain. In the last years, increasing efforts are being spent to study and characterize the microplastics found in nature, particularly in aquatic environments. At the same time, there is a need to advance the instruments and techniques necessary to perform fast, quantitative, and reliable analysis of microplastics. In this framework, we present a compact and cost-effective holographic microscope, integrated with a commercial microfluidic chip to visualize and evaluate microplastic samples in flowing conditions. The preliminary results show that different microplastic samples can be clearly imaged in-flow through a Digital Holography microscope. This is the first step for assessing novel strategies for the detection and identification of microplastics.

Biospeckle Analysis and Biofilm Electrostatic Tests, Two Useful Methods in Microbiology

Abstract: The development of more sensitive methodologies, capable of quickly detecting and monitoring a microbial population present in a specific biological matrix, as well as performing to allow for the study of all its metabolic changes (e.g., during the formation of biofilm) to occur, is an essential requirement for both well-being and the food industry. Two techniques, in particular, have gained the attention of scientists: The first is “biospeckle”, an optical technique representing an innovative tool for applications in food quality, food safety, and nutraceuticals. With this technique, we can quickly evaluate and monitor the presence of bacteria (or their proliferation) in a solid or liquid biological matrix. In addition, the technique is helpful in quantifying and optimizing the correct storage time of the pro-biotics, if they are entrapped in matrices such as alginate and follow their survival rate in simulated gastro-intestinal conditions. A second technique with great chances is the “biofilm electrostatic test” (BET). BET undoubtedly represents a fast, simple, and highly reproducible tool suitable for admitting the evaluation of the in vitro bacterial capacity in order to adhere through an electrostatic interaction with a pyro-electrified carrier after only 2 h of incubation. BET could represent the way for a quick and standardized evaluation of bacterial resistance among biofilm-producing microorganisms through a fast evaluation of the potential presence of the biofilm.

Damage evaluation of repeated impact events on glass/carbon hybrid composites

Abstract: Drop-weight experiments studied the damages due to repeated low-velocity impacts of hybrid composite laminates. The laminate made by carbon woven fabric and glass woven fabric impregnated by vinyl ester resin was subjected at single impact, at 5 repeated impacts and 10 repeated impacts, for an energetic level of U=20J. The final damage after the single and repeated impact events was analysed by no-destructive methods, Pulse Thermography and Holographic Interferometry to evaluate the influence of the multi-hit events on the damaged area's evolution.

Tomographic flow cytometry as the key-enabling technology for label-free liquid biopsy

Abstract: Liquid biopsy (LB) is a promising oncological tool that aims to the early diagnosis of tumors and to improve the therapeutical efficacy by following patient treatment. It is based on the detection and analysis of circulating tumor cells (CTCs) released into the bloodstream from primary or metastatic tumors. However, a reliable label-free LB tool has not yet been developed. Therefore, here we discuss the advantages derived from combining microfluidics and label-free quantitative imaging, to access the full three-dimensional (3D) information of a biological specimen by performing the tomographic reconstruction at single-cell level in high throughput modality. Experimental results on some CTCs are reported.

Label-free microfluidic platform for blood analysis based on phase-contrast imaging

Abstract: A lab-on-chip platform for blood cells analysis will be presented that combine phase-contrast label-free imaging, microfluidics and artificial intelligence. Such platform will have important impact in the aerospace field quantifying the effect on the blood cells of astronauts stresses. Furthermore, smart and innovative platform for blood analysis could be the key element for innovative biomedicine and telemedicine solutions in aerospace applications.

Biocompatible micro-needles for smart therapy

Abstract: Here we describe the fabrication of smart biodegradable and biocompatible micro-needles for combining the drug delivery with advanced sensing properties that would be the base for advanced telemedicine tools.

Tracking-based rolling angles recovery method for holographic tomography of flowing cells

Abstract: Holographic Tomography (HT) is an emerging label-free technique for microscopic bioimaging applications, that allows reconstructing the three-dimensional (3D) refractive index (RI) distribution of biological specimens. Recently, an in-flow HT technique has been proposed in which multiple digital holograms are recorded at different viewing angles around the sample while it flows and rotates within a microfluidic channel. However, unlike conventional HT methods, there is no a priori information about cell 3D orientations, that are instead requested to perform any tomographic algorithm. Here we investigate a tracking-based rolling angles recovery method, showing robustness against the sample9s features. It is based on a phase images similarity metric recently demonstrated, that exploits the local contrast phase measurements to recognize a full cell rotation within the microfluidic channel. Hence, the orientations of the flowing cells are retrieved from their positions, which are in turn computed through the 3D holographic tracking. The performances of the rolling angles recovery method have been assessed both numerically, by simulating a 3D cell phantom, and experimentally, by reconstructing the 3D RI tomograms of two cancer cells. Both the numerical and the experimental analysis have been performed at different spatial resolutions. This rolling angles recovery method, not depending on the cell shapes, the RI contents, and the optical experimental conditions, could pave the way to the study of circulating tumor cells (CTCs) in the challenging tool of liquid biopsy.

Raw holograms based machine learning for cancer cells classification in microfluidics

Abstract: We investigate the ability of machine learning to provide an accurate classification of cancer cell in microfluidics when only raw digital holograms are used as input data. Comparison among different learning strategies is addressed.

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning

Abstract: About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.